Literature DB >> 33151063

Advancing Physical Chemistry with Machine Learning.

Oleg V Prezhdo.   

Abstract

Year:  2020        PMID： 33151063     DOI： 10.1021/acs.jpclett.0c03130

Source DB:  PubMed          Journal:  J Phys Chem Lett        ISSN： 1948-7185            Impact factor:   6.475


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  7 in total

Review 1.  Ab Initio Machine Learning in Chemical Compound Space.

Authors:  Bing Huang; O Anatole von Lilienfeld
Journal:  Chem Rev       Date:  2021-08-13       Impact factor: 60.622

Review 2.  Recent Advances in Sequential Infiltration Synthesis (SIS) of Block Copolymers (BCPs).

Authors:  Eleonora Cara; Irdi Murataj; Gianluca Milano; Natascia De Leo; Luca Boarino; Federico Ferrarese Lupi
Journal:  Nanomaterials (Basel)       Date:  2021-04-13       Impact factor: 5.076

3.  AI-driven prediction of SARS-CoV-2 variant binding trends from atomistic simulations.

Authors:  Sara Capponi; Shangying Wang; Erik J Navarro; Simone Bianco
Journal:  Eur Phys J E Soft Matter       Date:  2021-10-06       Impact factor: 1.890

4.  Family of Two-Dimensional Transition Metal Dichlorides: Fundamental Properties, Structural Defects, and Environmental Stability.

Authors:  Andrey A Kistanov; Stepan A Shcherbinin; Romain Botella; Artur Davletshin; Wei Cao
Journal:  J Phys Chem Lett       Date:  2022-03-01       Impact factor: 6.475

5.  Low-cost prediction of molecular and transition state partition functions via machine learning.

Authors:  Evan Komp; Stéphanie Valleau
Journal:  Chem Sci       Date:  2022-06-14       Impact factor: 9.969

6.  Physico-chemical properties of selenium-tellurium alloys across the scales.

Authors:  Luke D Geoffrion; Grégory Guisbiers
Journal:  Nanoscale Adv       Date:  2021-05-28

7.  LGB-Stack: Stacked Generalization with LightGBM for Highly Accurate Predictions of Polymer Bandgap.

Authors:  Kai Leong Goh; Atsushi Goto; Yunpeng Lu
Journal:  ACS Omega       Date:  2022-08-15
  7 in total

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